AWS Senior Data Engineer

As a senior data engineer at a US-based healthcare analytics SaaS company, your role will involve owning the data platform end-to-end. The primary task is to migrate a substantial legacy SQL Server stored procedure codebase into a modern, maintainable, and testable pipeline architecture. You will work on AWS-hosted SQL Server, AWS infrastructure, and Airflow-based ETL pipelines to support both internal operations and client-facing data onboarding. Your responsibilities will include: - Auditing and refactoring legacy T-SQL logic - Designing and maintaining ETL/ELT pipelines for claims, eligibility, pharmacy, and premium datasets - Owning SQL Server administration tasks such as query tuning, HA/DR, and schema management - Building data validation, anomaly detection, and reconciliation checks across critical pipelines - Maintaining technical documentation including data dictionaries, lineage diagrams, and runbooks To excel in this role, you should have: - 5+ years of experience in data engineering or senior DBA roles in production environments - Expert-level T-SQL skills including complex queries, window functions, CTEs, performance tuning, and execution plan analysis - Demonstrated experience in refactoring legacy stored procedure logic into modern pipeline architectures using tools like dbt, Python, and Airflow - Proficiency in Python for ETL, pipeline components, and data validation - Production experience with Apache Airflow and dbt - Working knowledge of AWS services such as S3, RDS, or equivalent, and SQL Server on AWS - Practical experience with SOC2 and HIPAA requirements in real production contexts Please note that this job is remote with flexible work timings, and US overlap is preferred. If you are passionate about data engineering and enjoy working autonomously to drive transformative AI-powered solutions in the healthcare analytics domain, we encourage you to apply for this exciting opportunity with a stealth-mode startup represented by InCommon. As a senior data engineer at a US-based healthcare analytics SaaS company, your role will involve owning the data platform end-to-end. The primary task is to migrate a substantial legacy SQL Server stored procedure codebase into a modern, maintainable, and testable pipeline architecture. You will work on AWS-hosted SQL Server, AWS infrastructure, and Airflow-based ETL pipelines to support both internal operations and client-facing data onboarding. Your responsibilities will include: - Auditing and refactoring legacy T-SQL logic - Designing and maintaining ETL/ELT pipelines for claims, eligibility, pharmacy, and premium datasets - Owning SQL Server administration tasks such as query tuning, HA/DR, and schema management - Building data validation, anomaly detection, and reconciliation checks across critical pipelines - Maintaining technical documentation including data dictionaries, lineage diagrams, and runbooks To excel in this role, you should have: - 5+ years of experience in data engineering or senior DBA roles in production environments - Expert-level T-SQL skills including complex queries, window functions, CTEs, performance tuning, and execution plan analysis - Demonstrated experience in refactoring legacy stored procedure logic into modern pipeline architectures using tools like dbt, Python, and Airflow - Proficiency in Python for ETL, pipeline components, and data validation - Production experience with Apache Airflow and dbt - Working knowledge of AWS services such as S3, RDS, or equivalent, and SQL Server on AWS - Practical experience with SOC2 and HIPAA requirements in real production contexts Please note that this job is remote with flexible work timings, and US overlap is preferred. If you are passionate about data engineering and enjoy working autonomously to drive transformative AI-powered solutions in the healthcare analytics domain, we encourage you to apply for this exciting opportunity with a stealth-mode startup represented by InCommon.